
'''simple docstring'''

from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_UpperCAmelCase :   Any			    =	logging.get_logger(__name__)

class       UpperCAmelCase					(       __lowerCAmelCase			):





		"""simple docstring"""





		A__	: Union[str, Any]    =     ['pixel_values']
		def __init__(     self	,       _snake_case = True	,       _snake_case = None	,       _snake_case = PILImageResampling.BICUBIC	,       _snake_case = True	,       _snake_case = None	,       _snake_case = True	,       _snake_case = 1 / 255	,       _snake_case = True	,       _snake_case = IMAGENET_DEFAULT_MEAN	,       _snake_case = IMAGENET_DEFAULT_STD	,       **_snake_case	,       )					->     List[str]:
				super().__init__(**lowerCamelCase_  )
				_UpperCamelCase							:			Union[str, Any]     =   size if size is not None else {'''shortest_edge''': 224}
				_UpperCamelCase							:			str     =   get_size_dict(lowerCamelCase_	,       default_to_square=lowerCamelCase_  )
				_UpperCamelCase							:			str     =   crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
				_UpperCamelCase							:			str     =   get_size_dict(lowerCamelCase_	,       param_name='''crop_size'''  )

				_UpperCamelCase							:			Tuple     =   do_resize
				_UpperCamelCase							:			List[str]     =   size
				_UpperCamelCase							:			Optional[Any]     =   resample
				_UpperCamelCase							:			Union[str, Any]     =   do_center_crop
				_UpperCamelCase							:			Tuple     =   crop_size
				_UpperCamelCase							:			int     =   do_rescale
				_UpperCamelCase							:			Optional[Any]     =   rescale_factor
				_UpperCamelCase							:			Optional[Any]     =   do_normalize
				_UpperCamelCase							:			str     =   image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
				_UpperCamelCase							:			List[str]     =   image_std if image_std is not None else IMAGENET_DEFAULT_STD
		def _lowercase	(     self	,       _snake_case	,       _snake_case	,       _snake_case = PILImageResampling.BICUBIC	,       _snake_case = None	,       **_snake_case	,       )					->     List[str]:
				_UpperCamelCase							:			Optional[int]     =   get_size_dict(lowerCamelCase_	,       default_to_square=lowerCamelCase_  )
				# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
				if "shortest_edge" in size:
						_UpperCamelCase							:			List[str]     =   int((256 / 224) * size['''shortest_edge''']  )
						_UpperCamelCase							:			Dict     =   get_resize_output_image_size(lowerCamelCase_	,       size=lowerCamelCase_	,       default_to_square=lowerCamelCase_  )
						_UpperCamelCase							:			List[Any]     =   {'''height''': output_size[0], '''width''': output_size[1]}
				if "height" not in size_dict or "width" not in size_dict:
						raise ValueError(
						    F'''Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'''  )
				return resize(
				    lowerCamelCase_	,       size=(size_dict['''height'''], size_dict['''width'''])	,       resample=lowerCamelCase_	,       data_format=lowerCamelCase_	,       **lowerCamelCase_  )
		def _lowercase	(     self	,       _snake_case	,       _snake_case	,       _snake_case = None	,       **_snake_case	,       )					->     Optional[Any]:
				_UpperCamelCase							:			List[Any]     =   get_size_dict(lowerCamelCase_  )
				if "height" not in size or "width" not in size:
						raise ValueError(F'''Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'''  )
				return center_crop(lowerCamelCase_	,       size=(size['''height'''], size['''width'''])	,       data_format=lowerCamelCase_	,       **lowerCamelCase_  )
		def _lowercase	(     self	,       _snake_case	,       _snake_case	,       _snake_case = None	,       **_snake_case	,       )					->     int:
				return rescale(lowerCamelCase_	,       scale=lowerCamelCase_	,       data_format=lowerCamelCase_	,       **lowerCamelCase_  )
		def _lowercase	(     self	,       _snake_case	,       _snake_case	,       _snake_case	,       _snake_case = None	,       **_snake_case	,       )					->     List[str]:
				return normalize(lowerCamelCase_	,       mean=lowerCamelCase_	,       std=lowerCamelCase_	,       data_format=lowerCamelCase_	,       **lowerCamelCase_  )




		def _lowercase	(     self	,       _snake_case	,       _snake_case = None	,       _snake_case = None	,       _snake_case = None	,       _snake_case = None	,       _snake_case = None	,       _snake_case = None	,       _snake_case = None	,       _snake_case = None	,       _snake_case = None	,       _snake_case = None	,       _snake_case = None	,       _snake_case = ChannelDimension.FIRST	,       **_snake_case	,       )					->     Tuple:
				_UpperCamelCase							:			Optional[Any]     =   do_resize if do_resize is not None else self.do_resize
				_UpperCamelCase							:			Dict     =   resample if resample is not None else self.resample
				_UpperCamelCase							:			str     =   do_center_crop if do_center_crop is not None else self.do_center_crop
				_UpperCamelCase							:			Dict     =   do_rescale if do_rescale is not None else self.do_rescale
				_UpperCamelCase							:			Optional[Any]     =   rescale_factor if rescale_factor is not None else self.rescale_factor
				_UpperCamelCase							:			Dict     =   do_normalize if do_normalize is not None else self.do_normalize
				_UpperCamelCase							:			Tuple     =   image_mean if image_mean is not None else self.image_mean
				_UpperCamelCase							:			List[str]     =   image_std if image_std is not None else self.image_std

				_UpperCamelCase							:			str     =   size if size is not None else self.size
				_UpperCamelCase							:			int     =   get_size_dict(lowerCamelCase_	,       default_to_square=lowerCamelCase_  )
				_UpperCamelCase							:			List[str]     =   crop_size if crop_size is not None else self.crop_size
				_UpperCamelCase							:			Union[str, Any]     =   get_size_dict(lowerCamelCase_	,       param_name='''crop_size'''  )

				_UpperCamelCase							:			Optional[Any]     =   make_list_of_images(lowerCamelCase_  )

				if not valid_images(lowerCamelCase_  ):
						raise ValueError(
						    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
						    '''torch.Tensor, tf.Tensor or jax.ndarray.'''  )

				if do_resize and size is None:
						raise ValueError('''Size must be specified if do_resize is True.'''  )

				if do_center_crop and crop_size is None:
						raise ValueError('''Crop size must be specified if do_center_crop is True.'''  )

				if do_rescale and rescale_factor is None:
						raise ValueError('''Rescale factor must be specified if do_rescale is True.'''  )

				if do_normalize and (image_mean is None or image_std is None):
						raise ValueError('''Image mean and std must be specified if do_normalize is True.'''  )

				# All transformations expect numpy arrays.
				_UpperCamelCase							:			Union[str, Any]     =   [to_numpy_array(lowerCamelCase_  ) for image in images]

				if do_resize:
						_UpperCamelCase							:			Dict     =   [self.resize(lowerCamelCase_	,       lowerCamelCase_	,       lowerCamelCase_  ) for image in images]

				if do_center_crop:
						_UpperCamelCase							:			Optional[Any]     =   [self.center_crop(lowerCamelCase_	,       lowerCamelCase_  ) for image in images]

				if do_rescale:
						_UpperCamelCase							:			str     =   [self.rescale(lowerCamelCase_	,       lowerCamelCase_  ) for image in images]

				if do_normalize:
						_UpperCamelCase							:			Optional[Any]     =   [self.normalize(lowerCamelCase_	,       lowerCamelCase_	,       lowerCamelCase_  ) for image in images]

				_UpperCamelCase							:			Any     =   [to_channel_dimension_format(lowerCamelCase_	,       lowerCamelCase_  ) for image in images]

				_UpperCamelCase							:			Optional[int]     =   {'''pixel_values''': images}
				return BatchFeature(data=lowerCamelCase_	,       tensor_type=lowerCamelCase_  )






def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	list:







 '''simple docstring'''
 UpperCamelCase			  =      len(UpperCamelCase_       )
 UpperCamelCase			  =      [[0] * n for i in range(UpperCamelCase_       )]
 for i in range(UpperCamelCase_       ):
  UpperCamelCase			  =      y_points[i]

 for i in range(2							,					UpperCamelCase_       ):
  for j in range(UpperCamelCase_							,					UpperCamelCase_       ):
   UpperCamelCase			  =      (
       (xa - x_points[j - i + 1]) * q[j][i - 1]
       - (xa - x_points[j]) * q[j - 1][i - 1]
   ) / (x_points[j] - x_points[j - i + 1])

 return [q[n - 1][n - 1], q]


if __name__ == "__main__":
     import doctest

     doctest.testmod()
